def test_pca(self):
globalPCA = model_order_reduction.PCA(self.X)
globalPCA.eigens = self.nPCtest
globalPCA.plot_explained_variance = False
PCs, eigenvalues = globalPCA.fit()
explained = globalPCA.get_explained()
self.assertEqual(PCs.shape[1],self.nPCtest)
self.assertEqual(len(eigenvalues),self.nPCtest)
self.assertIsInstance(explained, float)
text_file.close()
# Retrieve the PVs name, and the corresponding numbers in the original matrix X
PV = model_order_reduction.variables_selection(X_preprocessed, settings)
PVs, numbers = PV.fit()
# Save the selected variables in a .txt, they can be useful later on
np.savetxt("Selected_variables.txt", numbers)
# Cut the dimensionality of the original matrix, considering only
# the selected 'm' variables
X_preprocessed_cut = X_preprocessed[:, numbers]
#perform the dimensionality reduction via Principal Component Analysis,
# and return the eigenvectors of the reduced manifold (PCs).
model = model_order_reduction.PCA(X_preprocessed, settings)
PCs, ____ = model.fit()
#compute the projection of the original points on the reduced
#PCA manifold, obtaining the scores matrix Z
Z = model.get_scores()
# Compute the matrix B for the MGPCA reconstruction
Bi, resid, rank, s = np.linalg.lstsq(X_preprocessed_cut, Z)
# Now we can reconstruct the original matrix from the reduced matrix X_preprocessed_cut
X_recovered = X_preprocessed_cut @ Bi @ PCs.T
# Uncenter and unscale the reconstructed matrix
X_ = unscale(X_recovered, sigma)
X_back = uncenter(X_, mu)
"scale": True,
"scaling_method": "auto",
#set the final dimensionality
"number_of_eigenvectors": 7,
#enable to plot the cumulative explained variance
"enable_plot_variance": True,
#set the number of the variable whose reconstruction must be plotted
"variable_to_plot": 0,
}
X = readCSV(file_options["path_to_file"], file_options["input_file_name"])
model = model_order_reduction.PCA(X, settings)
#perform the dimensionality reduction via Principal Component Analysis,
#and return the eigenvectors of the reduced manifold
PCs, ____ = model.fit()
#plot the original PC
model.plot_PCs()
#apply the varimax rotation algorithm from the utilities module
rotated = varimax_rotation(X, PCs, normalize=False)
#plot the rotated PC
fig = plt.figure()
axes = fig.add_axes([0.15, 0.15, 0.7, 0.7], frameon=True)
x = np.linspace(1, X.shape[1], X.shape[1])
axes.bar(x, rotated[:, 0])
